This document summarizes how Twitter used DTrace to investigate performance issues. DTrace helped Twitter identify that 40% of CPU time was spent in backtrace() calls in Ruby code, due to unnecessary exception handling. Removing these checks led to a 30% drop in CPU usage. DTrace also found that rewriting a CRC32 computation from Ruby to C could yield a further 15% CPU reduction. In general, DTrace allows identifying performance bottlenecks and improving application efficiency.

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Observability Matters:
How DTrace Helped Twitter
Adam Leventhal
Brendan Gregg
FishWorks
Sun Microsystems
a version of this presentation with the demo materials will be available shortly after the conference at http://blogs.sun.com/ahl

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This Talk
Twitter performance meltdown
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Introduction to DTrace
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DTrace on the case
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Solutions and results
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DTrace for your application
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Q&A
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What Is Twitter?
Social networking/RSS/SMS
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Ruby on Rails application
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Horizontally scaled
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Centralized MySQL backend
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Twitter's Problem
Started with a few users
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Didn't scale with their success
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High latencies could make it unusable
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Many possible suspects:
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The OS: kernel, libraries, etc.
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Ruby, MySQL, Apache
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The Twitter application itself
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Enter DTrace
Luckily: Twitter had DTrace
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Solaris 10, Mac OS X 10.5, FreeBSD*
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Systemic observability
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Ruby, Java, JavaScript, C/C++, kernel ...
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Concise answers to arbitrary questions
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Designed for production systems
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Architected to always be safe
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No overhead when not in use
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Also great for developers
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DEMO
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The Investigation
High latencies under load
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What could cause these latencies?
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on-CPU time: slow/numerous functions, ...
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off-CPU time: I/O, lock contention, ...
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DEMO
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Investigation: Steps
1. Started with a broad look at the system
using familiar tools
mpstat, ...
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2. DTrace profiling to sample processes
3. DTrace profiling to sample user stacks
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Investigation: So Far
What we knew:
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High application latency (from somewhere)
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What we learned:
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Latency may be due to high CPU load
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CPU load is due to some Ruby component:
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Twitter application
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Standard Ruby libraries
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Interpreter itself
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DEMO
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Investigation: Steps
4. Function call counts
5. ustack() for memcpy()
6. Time spent in backtrace()
What we learned:
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40% of CPU time spent in backtrace()
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DEMO
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Investigation: Steps
7. Located source of backtrace() calls in
Ruby code
What we learned:
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Several instances of code like this:
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@string = (str.string rescue str)
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Results: No Exceptions
Rather than blithely calling the method:
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@string = (str.string rescue str)
... check first:
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@string = str.responds_to?(:string) ?
str.string : str
Result: 30% drop in CPU utilization
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Ruby Provider
Joyent built DTrace-enabled Ruby
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Trace function entry and return
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Probes for line execution, memory
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allocation, etc.
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DEMO
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Results: CRC32 in C
Precision optimization
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Rewrite the CRC32 computation in C
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Estimated result: 15% drop in CPU
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utilization
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DTrace Your Application
Get your application on an OS with
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DTrace
Start with the tools you know and dive
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deeper with DTrace
If you can do it today or tomorrow, find
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Adam and Brendan: we'll help you
investigate
DTrace sees all
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Q&A (and links)
DTrace home page
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http://www.opensolaris.org/os/community/dtrace
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DTrace-enabled Ruby
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http://dtrace.joyent.com
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Getting started with DTrace
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http://blogs.sun.com/ahl/entry/dtrace_boot_camp
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Exception problem:
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http://dev.rubyonrails.org/ticket/8159
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http://dev.rubyonrails.org/changeset/6571
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Joyent's blog post on this subject
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http://joyeur.com/2007/04/24/solaris-dtrace-and-rails 20
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Observability Matters:
How DTrace Helped Twitter
Adam Leventhal
http://blogs.sun.com/ahl
Brendan Gregg
http://blogs.sun.com/brendan